Top 5 AI Network Monitoring Use Cases and Real Life Examples
The integration of artificial intelligence (AI) into network monitoring enhances availability and performance.
Here are options for you:
- If you’re searching for an AI network monitoring tool, compare the top options.
- Are you interested in discovering how AI is enhancing network monitoring? Explore how AI technologies streamline operations.
AI Network Monitoring Tools
Vendors | Reviews | Number Of Employees | Free Trial | Pricing |
|---|---|---|---|---|
NinjaOne | 4.7 based on 1,428 reviews | 1,219 | ✅ (14-day) | Not shared publicly. |
Dynatrace | 4.4 based on 1,494 reviews | 5,018 | ✅ (15-day) | Full-Stack: $0.08 per hour / 8 GiB host Infrastructure: $0.04 per hou Application Security: $0.018 per hour / 8 GiB host Real User: $0.00225 Per session Synthetic: $0.001 Per synthetic request |
LogicMonitor | 4.5 based on 843 reviews | 1,122 | ✅ (14-day) | Infrastructure Monitoring: $22 USD per resource/month Cloud IaaS Monitoring: $22 USD per resource/month and more options. |
Auvik | 4.3 based on 508 reviews | 346 | ✅ (14-day) | Not shared publicly. |
** Reviews are based on Capterra and G2. Vendors are ranked according to their number of reviews
*** Free trial periods and pricing are included if the content is publicly shared.
Real-life Examples
Case study #1 from Juniper Networks
Source: AI-Native Networking Diagram1
Juniper Networks makes enterprise networking equipment. They also had network problems in their own automated warehouse—WiFi kept dropping, affecting operations.2
Traditional WiFi monitoring couldn’t pinpoint why connectivity failed in the warehouse. Engineers spent hours manually troubleshooting VLAN configurations and DHCP settings.
Juniper’s own Mist AI platform with Marvis Virtual Network Assistant3 .
Marvis automatically identified VLAN misconfigurations and DHCP errors, which would have taken engineers hours to find in logs.
Results:
- 90% fewer support tickets
- 50% faster problem resolution
- 85% fewer on-site visits from technicians
Case study #2: DataDog
Toyota’s manufacturing plants use Automated Guided Vehicles (AGVs) to move parts around. These AGVs kept disconnecting from the network, stopping production4 .
AGVs would randomly disconnect. The AGV vendor blamed Toyota’s network. Toyota’s team couldn’t find the issue after weeks of investigation.
Datadog’s Watchdog AI engine for real-time monitoring and root cause analysis.
Watchdog analyzed network and infrastructure data in real-time. It correlated disconnection patterns with specific network events that weren’t obvious from manual log review.
Results:
- Saved thousands in lost production time
- Solved in hours what took weeks before
- Mean time to resolution: 6 hours to 15 minutes (large-scale system)
- Another Toyota plant: MTTR from 7 days to 2 hours
Case study #3 from Dynatrace Davis AI
Dynatrace offers an AI network monitoring engine called Davis, which is integral to its software intelligence platform. Davis analyzes data across the digital ecosystem, including clouds, applications, and infrastructure.
BARBRI provides bar exam preparation courses. They moved from on-premises servers to Azure cloud and needed visibility into their new environment.
BARBRI needed to scale Azure resources during peak exam seasons (thousands of students logging in simultaneously). Manual monitoring couldn’t keep up with dynamic cloud infrastructure.
Dynatrace with Davis AI engine integrated with Azure Monitor.
Davis learned BARBRI’s normal traffic patterns and automatically adjusted monitoring as they scaled Azure resources up or down. It provided real-time insights and root cause analysis when issues occurred.
Results:
- Successful full migration to Azure
- Real-time visibility during peak scaling events
- Better communication with executives using AI-generated insights
By bringing in metrics from Azure Monitor, the Dynatrace AI engine now provides better answers, to give us a deeper view into service behavior and root cause,” said Mark Kaplan, Senior Director of IT at BARBRI5 .
Source: Dynatrace Davis AI User Interface6
Case Study #4 from Cisco AI Network Analytics
Source: Cisco AI Network Analytics Features 7 .
REWE Group (a German retail and tourism company) implemented Cisco AI Network Analytics to speed up network troubleshooting.
Cisco’s DNA Center uses machine learning to predict network issues and detect unusual patterns that indicate security threats or performance problems.
Reduced time to resolve network issues, freeing up IT staff to work on new projects instead of firefighting. AI simplified daily network management and highlighted critical alerts while filtering out noise8 .
Case study #5 from Anadot
LivePerson runs a conversational AI platform serving global customers 24/7. They monitor nearly 2 million metrics every 30 seconds across data centers worldwide.
Manual monitoring couldn’t catch anomalies fast enough across millions of data points. By the time engineers noticed issues, customers were already affected.
Anodot’s real-time AI analytics. Anodot’s machine learning automatically identifies deviations from expected patterns, alerting engineers to issues before they impact customers. Maintained 24/7 uptime and caught problems in real-time instead of after customer complaints9 .
AI Use Cases In Network Monitoring
Based on these real deployments, here’s what AI handles:
Spots unusual patterns: Detects deviations from normal network behavior, potential security breaches, or system failures that don’t trigger traditional threshold alerts.
Predicts failures: Analyzes historical data to predict network failures or performance drops before they happen. Toyota went from reacting to problems to preventing them.
Automates configuration: Adjusts network settings based on current traffic patterns. BARBRI’s Azure environment scaled automatically during exam periods.
Finds threats faster: Identifies malware, ransomware, and malicious activities in real-time by recognizing attack patterns.
Diagnose root causes: Correlate data points to find the actual problem source. Juniper’s Marvis found VLAN misconfigurations that would have taken engineers hours to locate manually.
Plans capacity: Forecasts future network needs based on growth trends, helping you plan upgrades before hitting limits.
AI Network Monitoring Tools
1. Dynatrace
Dynatrace’s Davis AI engine automates root cause analysis, anomaly detection, and gives you predictive insights before problems hit users.
AI features:
- Automatically discovers dependencies between applications, services, and infrastructure
- Maps network topology in real-time as your environment changes
- Predicts performance issues and capacity constraints using ML models
- Can automatically fix common performance issues without human intervention
Best for: Companies with complex, dynamic cloud environments where manual troubleshooting takes too long.
2.LogicMonitor
LogicMonitor uses AI to detect anomalies before they become critical issues. Their predictive analytics help IT teams fix problems proactively.
AI features:
- Reduces alert noise by correlating related alerts and prioritizing by actual impact
- Forecasts resource utilization and capacity needs using ML
- Adjusts monitoring thresholds automatically based on your historical patterns (not static rules)
Best for: Teams drowning in alerts who need smarter filtering.
3. Auvik
Auvik is built for Managed Service Providers managing multiple client networks. Their AI handles discovery and anomaly detection automatically.
AI features:
- Auto-discovers and maps network topology as devices come and go
- Identifies unusual network behavior patterns using ML
- Smart alert management cuts down noise
- Gives predictive insights for proactive maintenance
Best for: MSPs managing dozens of different client networks.
4. NinjaOne
NinjaOne focuses on automation network discovery, device monitoring, and patch management that run without manual intervention.
AI features:
- Automated anomaly detection and alerts
- Predictive analytics to catch problems before escalation
- Automates routine tasks (discovery, monitoring, patching)
Best for: Small IT teams that need automation to handle more with fewer people.
5. Datadog
Datadog monitors modern, cloud-native infrastructure. Watchdog, their AI engine, surfaces issues you didn’t know to look for.
AI features:
- Identifies unusual patterns in metrics, logs, and traces using ML
- Correlates related events and prioritizes by business impact
- Forecasting for capacity planning and trend analysis
- Watchdog Insights automatically surfaces performance issues and optimization opportunities
Best for: DevOps teams running microservices, containers, or serverless architectures.
6.HPE Juniper: Mist AI
Juniper’s Mist AI platform includes Marvis Virtual Network Assistant, which you can ask questions like “Why is Building 3 WiFi slow?” in plain language.
AI features:
- Marvis VNA provides anomaly detection, root cause analysis, and prescriptive fixes
- Marvis Minis simulates user connections synthetically to test network configs before problems occur
- Large Experience Model (LEM) analyzes billions of data points from Zoom, Teams, and other collaboration apps to predict user experience issues
Best for: Organizations with large WiFi deployments where user experience matters more than just uptime.
FAQs for AI Network Monitoring
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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